Data Warehouse

Data Warehouse Logical Data Model

A Data warehouse logical data model describes model in more detail compared to the Data Warehouse conceptual data model. A data warehouse logical data model describes the data in as much detail as possible, this model does not describe how the model is implemented. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Data Warehouse Three-tier Architecture in Details Features of Data Warehouse Logical Data…

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April 23, 2017
Data Warehouse

Data Warehouse Conceptual Data Model

A Data warehouse conceptual data model is nothing but a highest-level relationships between the different entities (in other word different table) in the data model. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Data Warehouse Three-tier Architecture in Details Features of Data Warehouse Conceptual Data Model Following are the features of conceptual data model: This is initial or high level relation between different entities…

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April 23, 2017
Data Warehouse

Data Warehouse Fact Constellation Schema and Design

Basically, Data warehouse fact constellation schema is viewed as a collection of many star schemas. For each star schema or snowflake schema it is possible to create Fact Constellation schema. This schema is one of the widely used data warehouse design methodology and is also called Galaxy schema. Sophisticated application required the Fact constellation schemas. Read: Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Step by Step Guide to Dimensional Data Modeling Slowly Changing Dimensions (SCD) in Data Warehouse Rapidly Changing Dimension in Data Warehouse Representation…

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December 3, 2016
BigData

Hadoop Data Warehouse and Design Considerations

A data warehouse, also known as an enterprise data warehouse (EDW), is a large collective store of data that is used to make such data-driven decisions, thereby becoming one of the centrepiece of an organization’s data infrastructure. Hadoop Data Warehouse was challenge in initial days when Hadoop was evolving but now with lots of improvement, it is very easy to develop Hadoop data warehouse Architecture. This article will server as a guide to Hadoop data warehouse system design. Hadoop data warehouse integration is now a days become very much popular…

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October 26, 2016
Data Warehouse

Various Data Warehouse Design Approaches:Top-Down and Bottom-Up

Data Warehouse design approaches are very important aspect of building data warehouse. Selection of right data warehouse design could save lot of time and project cost. There are two different Data Warehouse Design Approaches normally followed when designing a Data Warehouse solution and based on the requirements of your project you can choose which one suits your particular scenario. These methodologies are a result of research from Bill Inmon and Ralph Kimball. Bill Inmon - Top-down Data Warehouse Design Approach “Bill Inmon” is sometimes also referred to as the "father…

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September 17, 2016
Data Warehouse

Different Extraction Methods in Data Warehouse

Extraction is the first step of the ETL(Extract, Transform and Load) process. Once the data is extracted, you can transform it and load to target data warehouse. Extraction is the process of extracting data from the source system for further use in the data warehouse environment. Related Reading: Data Warehouse Fact Constellation Schema and Design Star Schema model in Data Warehouse Snowflake Schem Model in Data Warehouse Data warehouse is an OLAP system, typically source system includes the transaction business processing application. For example, it could be sales order entry…

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September 16, 2016
Greenplum

Greenplum Architecture

Like IBM Netezza and Amazon Redshift, Greenplum database is a massively parallel processing (MPP) database server. Greenplum architecture is designed to manage large scale data warehouse for analytics and business intelligence needs. Like any other large scale data warehouse appliances, Greenplum works well with Dimensional modeling. Read: Star Schema Model in Data Warehouse Step By Step Guide to Dimensional Modeling Greenplum Architecture Overview The MPP environment shared nothing architecture is made up of two or more processor that work together to perform tasks. Each processor has its own memory, operation…

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August 19, 2016
Data Warehouse

Data Warehouse Snowflake Schema Model and Design

Data warehouse Snowflake schema is extension of star schema data warehouse design methodology, a centralized fact table references to number of dimension tables, however, one or more dimension tables are normalized i.e. dimension tables are connected with other dimension tables. Primary Keys from the dimensions flows into fact table as foreign key. Star Schema model in Data Warehouse Data Warehouse Fact Constellation Schema and Design Snowflake schema increases the level of normalization in data, the dimension table is normalized into multiple tables. This schema has a disadvantage in terms of data retrieval, we…

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August 3, 2016
Data Warehouse

Step by Step Guide to Dimensional Data Modeling

In this post, you will learn about the step by step guide to dimensional data modeling. You will see how to use dimensional modeling technique in real life scenarios. What is Dimensional data Modeling? Dimensional data modeling is one of the data modeling techniques used in data warehouse design. The main goal of this modeling is to improve the data retrieval, it is optimized for the SELECT operation. Dimensional data modelling is best suited for the data warehouse star and snow flake schema. Dimensional data modeling in data warehouse is different than the…

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August 1, 2016
Data Warehouse

Data Warehouse Star Schema Model and Design

Data warehouse Star schema is a popular data warehouse design and dimensional model, which divides business data into fact and dimensions. In this model, centralized fact table references many dimension tables and primary keys from dimension table flows into fact table as a foreign key. This entity-relationship diagram looks star, hence the name star schema. This model divides the business data into fact which holds the measurable data, and dimension that holds descriptive attributes related to the fact data. For examples, fact data includes price, quantity, weight measurements and related dimension attributes example includes product color, sales…

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July 30, 2016